Naval Surface Ship Design Optimization for Affordability
Sponsor: Katherine Drew, ONR 334, Office of Naval Research
and Dr. Wayne Neu, Virginia Tech
Naval ship concept design is traditionally an “ad hoc” process. Selection of design concepts for assessment is guided primarily by experience, design lanes, rules-of-thumb and imagination. Communication and coordination between design disciplines (hull form, structures, resistance, etc.) requires significant designer involvement and effort. Concept studies continue until resources or time run out. Critical elements missing from this process are:
· A consistent format and methodology for multi-objective decisions based on dissimilar objective attributes: specifically effectiveness, cost and risk. Mission effectiveness, cost and risk cannot logically be combined as in commercial decisions where discounted cost can usually serve as a suitable single objective. Multiple objectives must be presented separately, but simultaneously, in a manageable format for trade-off and decision making.
· An efficient and robust method to search the design space for optimal concepts.
· Practical and quantitative methods for measuring effectiveness. An Overall Measure of Effectiveness (OMOE) model or function is an essential prerequisite for optimization and design trade-off. This effectiveness can be limited to individual ship missions or extend to missions within a task group or larger context.
· Practical and quantitative methods for measuring risk. Overall risk includes schedule, production, technology performance and cost factors. It is measured using an Overall Measure of Risk (OMOR).
· An effective framework for transitioning and refining concept development in a multidisciplinary design optimization (MDO).
· A means of using the results of first-principle analysis codes at earlier stages of design.
This project develops a process, tools and models implemented in Model Center, a general purpose design environment and optimization program, to address these critical elements. Steps in this process are:
1. Concept Exploration. A multi-objective genetic optimization (MOGO) considers discrete major system decisions and top level requirements including payload, choice of propulsion system and power, range and speed, parent hull forms, hull materials, arrangement, and manning. Genetic algorithms (GAs) are able to explore a design space that is very non-linear, discontinuous, and bounded by a variety of constraints and thresholds. These attributes prevent application of mature gradient-based optimization techniques including Lagrange multipliers, steepest ascent methods, linear programming, non-linear programming and dynamic programming. GAs are also ideally-suited for multi-objective optimization since they develop a population of designs vice a single optimum. This population can be forced to spread-out over a non-dominated frontier of design alternatives as illustrated in Figure 1. A non-dominated solution, for a given problem and constraints, is a feasible solution for which no other feasible solution exists that is better in one attribute and at least as good in all others. The non-dominated frontier is the first product of this process.
Figure 1 - Two Objective Attribute Space
2. Customer selection of preferred design(s). There is no reason to pay more for the same effectiveness or accept less effectiveness for the same cost. Preferred designs must always be on the non-dominated frontier. The selection of a particular non-dominated design depends on the decision-maker’s preference for cost, effectiveness and risk. This preference may be affected by the shape of the frontier and cannot be rationally determined a priori. When considering three attributes, the non-dominated frontier is a surface. Points on this surface represent feasible ships, and can be mapped to specific design parameters. With such a surface, the full range of cost-risk-effectiveness possibilities can be presented to decision-makers, trade-off decisions can be made, and specific concepts can be chosen for further analysis. “Knees in the curve” can be seen graphically. "Knees" are significant changes in the slope of the frontier. It is often desirable to be at the top of a high effectiveness to cost slope. Up to this point a little more cost will buy a lot more effectiveness. Beyond it, the cost of more effectiveness is much higher.
3. Concept Development. Starting from selected concepts from Step 2, this step develops the selected concept designs in a multidisciplinary design optimization (MDO) using mission effectiveness, cost, risk or some weighted combination of these as a single-objective attribute. Appropriate constraints on effectiveness, cost and risk are included. Discrete design and requirement decisions made in Step 2 become bounds and constraints in Step 3. This allows the application of more traditional and efficient optimization methods.
Deliverables  through  detail the research accomplished in Phase 1 of this project, based on the 3-step process outlined above.
Task 1.0 Literature, Information and Data Search – Literature and data searches were completed for the following topics: 1) Overall Measure of Effectiveness (OMOE) and AHP validation ; 2) Risk approaches to naval ship design ; and 3) Methods for design with uncertainty [1,2,3,5]. Reference lists and summaries are included in these related theses.
Task 2.0 Multi-Objective Models and Probabilistic Design Optimization
Task 2.1 OMOE Model - A simplified methodology was developed for building an Overall Measure of Effectiveness (OMOE) model using the Analytical Hierarchy Process and Multi-Objective Value Theory [4,6]. A validation experiment was completed using the war-gaming software HARPOON3 , Figure 2. In this validation, ten student experts were “educated” in a series of war-gaming experiences. OMOE functions were developed using the OMOE methodology and expert opinion. The OMOE functions were then used to rank a series of surface combatant designs. This ranking is compared to the ranking results from a direct war-gaming comparison of the designs.
Figure 2 - Harpoon 3 Workspace
Figure 3 – Simulation Rank vs. Questionnaire Average Rank
An OMOE calculation using questionnaire averages most closely matched and provided a good prediction of direct simulation results, Figure 3.
Task 2.2 Cost Model - Engineering cost models must be reliable, practical and sensitive to the cost and performance impact of producibility enhancements. A baseline surface combatant cost model was developed using a modified weight-based approach . A more flexible model will be developed in Phase 2 using ACEIT (Automated Cost Estimating Integrated Tools). ACEIT is an automated architecture and framework for cost estimating. It is a government-developed tool that has been used to standardize and simplify the Life Cycle Cost estimating process in the government environment. Core features include a database to store technical and (normalized) cost data, a statistical package specifically tailored to facilitate cost estimating relationship (CER) development and a spreadsheet that promotes structured, systematic model development, and built-in government-approved inflation, learning, time phasing, documentation, sensitivity/what-if, risk and other analysis capabilities. Our task will be to adapt this general framework for concept development naval ship cost analysis including producibility. Cost uncertainty aspects will be integrated with Task 2.3.
Task 2.3 Risk and Uncertainty - The DoD Risk Management Guide requires risk assessment of acquisition performance, cost and schedule through the identification, subsequent analysis and prioritization of adverse program events based on their probability of occurrence and consequences. This type of risk assessment is very important in concept exploration and design when considering new technologies, unique processes and novel concepts. Uncertainty associated with the design process itself and the definition and selection of specific design alternatives can also have a significant impact on performance, cost and schedule risk. Inherent, statistical and modeling uncertainty, and uncertainty due to human error, must be considered in the design process, but uncertainty analysis requires a more detailed and computationally intensive probabilistic approach. It is most appropriate for post-exploration design optimization, after specific cost and performance goals and thresholds have been set, to maximize the probability of achieving these goals.
We have adopted a two-stage concept design strategy that uses a multi-objective optimization and simplified risk event approach for concept exploration, and a more rigorous multi-disciplinary optimization with uncertainty for concept development. Concept exploration identifies non-dominated design concepts and establishes the optimum relationship between effectiveness, cost and risk given a broad selection of technologies and design alternatives. Risk is defined using a separate objective attribute, an Overall Measure of Risk (OMOR), which specifically addresses the high-risk events associated with the selection of new technologies, processes and concepts. With this perspective, decision-makers may establish rational requirements, select technologies, narrow the design space, and establish a non-dominated concept baseline design or set of designs. Once these early decisions are made, concept development and the remaining design phases add detail, refine requirements and reduce risk. Optimization continues into concept development using uncertainty analysis with Confidence of Success (CoS) as the third objective attribute. Tim Mierzwicki (MS Ocean Engineering 2003) performed the initial risk and uncertainty literature search, developed the OMOR approach, and performed an initial OMOR case study [1,7]
Since the mid 1990’s, Mavris and associates at Georgia Tech have been exploring robust design techniques in the presence of parameter uncertainties accounted for by assigning probability distributions to selected model inputs. The output responses have been generated by Monte Carlo simulations performed either using the full model or a response surface approximation of its output. They have also had success using the Advanced Mean Value (AMV) method for calculating the cumulative distribution function (CDF) of the model response in aerospace multidisciplinary design applications. The AMV method has the virtue of requiring far fewer model runs than does a Monte Carlo simulation. It is one of several Fast Probability Integration methods developed by Southwest Research Institute and NASA Lewis Research Center.
The primary difference in our initial research is that we are concentrating on the uncertainty generated from the analysis process, the modeling uncertainty. For now, we assume the input variables to be deterministic with randomness coming only from the embedded uncertainties in the analyses.
Because of the multidisciplinary nature of a ship synthesis model, perturbations introduced in one analysis are carried forward to perturb the next and then on to the next and so on. To further complicate the issue, ship synthesis models typically require multiple iterations to balance the design. Each subsequent level of analysis may introduce its own uncertainty and inherit uncertainty from previous analyses. It is difficult to characterize this cascading of uncertainty through a highly nonlinear analysis.
We have examined several methods to obtain information on the output distributions more efficiently than through the Monte Carlo simulation. The family of variance reduction techniques described, for example, by Law and Kelton, are designed to obtain this information in a statistically efficient manner. Statistical efficiency deals with the precision of an estimator. If we are trying to describe an output random variable (say the cost of a design), we are interested in certain parameters that define its distribution; for example its mean and standard deviation.
We used the Advanced Mean Value method to determine statistics of the, now random, ship design characteristics that are calculated by our model. We then determined probabilities that a given design will have greater than any given level of OMOE or the probability that the cost will be less than some level. We can also determine the probability that the design will be feasible, i.e., it meets all the applicable constraints.
The confidence of success is the joint probability that a given design 1) is feasible, 2) that it will have a cost that is less than a given maximum cost and 3) that it will have an OMOE that is greater than a given required value. This confidence of success (CoS) can be treated as a third objective function in a genetic algorithm based optimization. Figure 4 is a three-dimensional Pareto frontier from which a ship designer can pick the design of his choice considering both the overall value of the design and the risk being taken at that point in the design space.
This work was performed primarily by Sandipan Ganguly (MS Ocean Engineering, 2002) and Emanuel Klasen .
Figure 4 – Non-Dominated (Pareto) Frontier with Confidence of Success
Task 2.4 Design Test Cases and Applications - The approach, methods and tools developed in Phase 1 were exercised in a number of case studies using a simple ship synthesis model [4,5,6,8,9], and the US Navy’s Advanced Ship Synthesis and Evaluation Tool (ASSET) [2,3] in the ModelCenter (MC) design environment. The simplified model case studies were performed primarily by undergraduate ocean engineering students. A Mixed-Language Programming (MLP) approach was used to interface with the ASSET software.
Component-based software construction has gained significant momentum and become a main focus of software engineering research and computing. Even though there are many standards available now for developing component-based applications, there are still applications where a single-language based approach is not suitable. Some of the actions that a program performs are best expressed in a particular language, and the choice of a programming language is strongly dictated by the programmer’s preference. The Mixed-Language Programming (MLP) approach was used to build component-based software systems, with a specific emphasis on the ship design problem . This approach was compared with a newer tool-based integration methodology of modeling and building component-based software applications, using tools such as Phoenix Integration’s ModelCenter and Analysis Server.
ModelCenter and ASSET were also applied to two ship design case studies, LHA(R), a replacement for the US Navy amphibious assault ship, and DDG-51, a destroyer class vessel [2,5]. Overall Measure of Effectiveness (OMOE) and lead ship acquisition cost were the objective attributes. Design feasibility was evaluated, and various ship parameter calculations were performed using ASSET. ASSET was integrated with the design optimization software DARWIN to obtain the non-dominated frontier over a range of acquisition cost, Figure 5. Model Center software was used to integrate ASSET and Darwin, Figure 6. VBScript components were used to run various ASSET modules, apply the trade study option configurations, and calculate the objective functions. Windows script components were developed to access the operating system and invoke ASSET.
Figure 5 – LHAR Non–Dominated Frontier
Figure 6 – DDG-51 ASSET Model in Model Center
Phase 1 of this project accomplished the following objectives:
Phase 2 will include more Response Surface Modeling (RSM), a more detailed Design of Experiments (DOE) and variable screening, and capabilities for more physics-based modeling and assessing the impact of new technologies. We would like to focus on multi-hull high speed ships, and on using the computationally intensive tools required for these ships including a Rankine panel method code for seakeeping and wave resistance, an extensive dynamic simulation for machinery system definition and performance, a structural finite element code and codes for accessing structural vulnerability and survivability. We will also continue uncertainty modeling and solution development in increasingly complex and realistic problems with more demanding requirements for computational efficiency. Phase 2 will also include work on a surface ship manning model and submarine applications.
 Mierzwicki, T. (2003), “Risk Index for Multi-Objective Design Optimization of Naval Ships”, MS Thesis, Department of Aerospace and Ocean Engineering, Virginia Tech, April 24, 2003.
 Neti, S.N. (2005), “Ship Design Optimization Using ASSET”, MS Thesis, Department of Aerospace and Ocean Engineering, Virginia Tech, February 10, 2005.
 Gunasekaran, Murali Krishnan (2003), “Component-Based Application Development Using a Mixed-Language Programming (MLP) Approach”, MS Thesis, Department of Computer Science, Virginia Tech, December 2003.
 Demko, D. (2005), “Tools for Multi-Objective and Multi-Disciplinary Optimization in Naval Ship Design”, MS Thesis, Department of Aerospace and Ocean Engineering, Virginia Tech, May 2005.
 Klasen, E. (2006), “Confidence of Success in Multi-Criteria Optimization of Multi-Disciplinary Ship Design Models”, Report, Department of Aerospace and Ocean Engineering, Virginia Tech, March 2006.
 Brown, A.J., Salcedo, J. (2003), "Multiple Objective Genetic Optimization In Naval Ship Design", Naval Engineers Journal, Vol. 115, No. 4, pp. 49-61.
 Mierzwicki, T., Brown, A.J. (2004), “Risk Metric for Multi-Objective Design of Naval Ships”, Naval Engineers Journal, Vol. 116, No. 2, pp. 55-71.
 Good, N., Brown, A.J. (2006), “Multi-Objective Concept Design of an Advanced
Logistics Delivery Ship”, to be presented at ASNE Joint Sea Basing
Symposium, March 2006,
 Stepanchick, J., Brown, A.J. (2006), “Revisiting DDGX/DDG-51 Concept Exploration”, to be presented at ASNE Day, June 19-20, 2006, Arlington, VA.